DocumentCode :
3582763
Title :
Infusing social data analytics into Future Internet applications for manufacturing
Author :
Biliri, Evmorfia ; Petychakis, Michael ; Alvertis, Iosif ; Lampathaki, Fenareti ; Koussouris, Sotirios ; Askounis, Dimitrios
Author_Institution :
Decision Support Syst. Lab., Nat. Tech. Univ. of Athens, Athens, Greece
fYear :
2014
Firstpage :
515
Lastpage :
522
Abstract :
Today, a new age of engagement and collaboration has emerged with the proliferation of usergenerated content in social networks and generally the Web 2.0, rendering it particularly difficult for enterprises to monitor and act upon all content following conventional data mining methodologies. In this paper, we present our approach for a Future Internet enabler (FITMAN Anlzer) that provides automated, social data analytics and aims at assisting enterprises in becoming more tuned to their customer needs and gaining insights into current and future trends to early embed them into product design. The FITMAN Anlzer implementation is domainindependent and allows any manufacturer to effectively train it based on his needs and create personalized reports to timely capture the right information. Our methodology includes trend analytics, polarity detection through machine learning, data querying through flexible reports and finally informative charts to visualize the results in order to help companies in their decision making procedures.
Keywords :
Internet; data analysis; data mining; data visualisation; decision making; groupware; learning (artificial intelligence); product design; production engineering computing; query processing; social networking (online); FITMAN Anlzer; Future Internet applications; Future Internet enabler; Web 2.0; collaboration; data mining methodologies; data querying; decision making procedures; informative charts; machine learning; manufacturing; polarity detection; product design; result visualization; social data analytics infusion; social networks; trend analytics; user generated content; Context; Data mining; Facebook; Market research; Media; Sentiment analysis; Twitter; natural language processing; opinion mining; sentiment analysis; social data analytics; social media monitoring; trend analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Systems and Applications (AICCSA), 2014 IEEE/ACS 11th International Conference on
Type :
conf
DOI :
10.1109/AICCSA.2014.7073242
Filename :
7073242
Link To Document :
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